ChatGPT SEO is the work of making your brand easier to understand, cite, and recommend in ChatGPT answers. At #10, we approach it through clear brand positioning, useful answers to buyer questions, verifiable proof, and technical search foundations. The goal is an accurate recommendation for the right buyer, not a guaranteed placement or simply using ChatGPT to write more SEO content.
For a growth or brand leader, the useful question is specific: when someone describes a problem your business solves, does ChatGPT include you among the options, and does it explain your fit correctly? That is different from asking whether your homepage ranks for a keyword. A rankings report cannot answer it on its own.
This guide focuses on earning visibility inside ChatGPT answers. For the wider discipline across AI search platforms, start with our generative engine optimization guide for ecommerce brands. Here, we will work through the brand facts, page decisions, and testing plan we would put in place for a ChatGPT-focused brief.
What changes when the goal is a recommendation rather than a ranking?
We separate three jobs in our search work. SEO makes a site findable. Answer engine optimization (AEO) makes its answers easier to extract. Generative engine optimization (GEO) focuses on whether the brand is understood, cited, and recommended. ChatGPT SEO belongs in that broader GEO work, supported by the other two disciplines.
Consider an illustrative buyer asking for a premium product with a particular use case, delivery requirement, and budget. Ranking for the product category does not demonstrate that the brand meets those conditions. A useful recommendation needs enough information to explain why it fits, where it does not, and what the buyer should verify.
For a B2B company, the same problem appears in compatibility, application requirements, implementation scope, and support. A page that says “solutions for every business” gives the buyer less to evaluate than a page that names the application, supported conditions, and limits.
Our position is that AI search needs its own editorial and measurement decisions. We keep the SEO foundations, but we do not assume that more rankings or more articles prove progress in ChatGPT.
Start with the questions that would put you on a shortlist
We begin with a narrow set of buying situations, not a list of every keyword in the category. The inputs should come from customer questions, sales conversations, product knowledge, and the search data the business actually has.
For a DTC brand, useful questions concern product selection, comparisons, suitability, shipping, and policies. For a B2B brand, they may concern applications, technical requirements, procurement, and ongoing support. The test is whether a good answer could reasonably move someone toward a purchase or inquiry.
We would build the initial ChatGPT test set around three types of question:
- Unbranded discovery: Which brands or suppliers fit a defined need? Do not include your company name when testing whether it is discovered independently.
- Comparison: How do the available options differ, and which tradeoffs matter for this buyer?
- Branded verification: What does your brand offer, who is it for, and what evidence supports its claims?
Keep the buyer’s constraints in the question. Broad prompts about the “best” brand are less useful for deciding what to improve than questions about a need you can genuinely serve. We would also keep prompted brand mentions separate from unprompted recommendations. Asking ChatGPT to discuss your company is not evidence that it would have suggested you.
Make your brand’s fit explicit before commissioning another article
We tie capability claims to actual engagement records. That is the discipline we want on a brand’s public site too: a claim should lead to evidence, not another version of the same claim.
We check whether the homepage, About page, product or service pages, and supporting content agree on what the business does and whom it serves. For a Shopify brand, that includes the relationship between products, collections, buying guidance, and policies. For a B2B WordPress site, it includes the connection between service scope, application pages, technical information, and proof.
A practical review asks:
- Can a reader identify the intended customer without interpreting brand language?
- Are product differences or service boundaries stated plainly?
- Are important suitability claims supported by specifications, documented work, or another verifiable source?
- Do internal links connect the answer to the relevant product, service, or evidence?
- Do older pages contradict the current offer?
We do not treat repeating the brand name as a substitute for this work. The useful unit is a clear statement of fit supported by facts. If the facts are missing, the next task is to obtain them from the business, not ask a writer to make the paragraph sound more authoritative.
Build answers around evidence a buyer can check
A direct-answer lead should resolve the question before the page expands into detail. It needs to name the subject, explain the recommendation or decision, and state any important limitation. The rest of the page supplies the reasoning and evidence.
For an illustrative product comparison, we would explain the relevant selection criteria, show which documented features meet them, and describe when the alternative is a better fit. For a service page, we would state what is included, what the client needs to provide, and which real engagement demonstrates the capability. These are editorial patterns, not claims that a particular ChatGPT response will cite them.
The evidence has to remain accurate when read outside its original paragraph. A technical capability should not become an unsupported performance claim. A case study showing what we built should not imply revenue growth we have not measured. A historical result should carry its period rather than read as a current condition.
That last distinction matters in our own proof.
What #10’s internal citation tracking actually demonstrates
We run a search measurement and content-planning system for nr10.com, including an intent map, keyword-to-intent reverse map, editorial calendar, rank analysis, and AI Overview citation tracking. The system separates decisions about what to write from observations about what search systems return.
In our June 2026 progress report, Google AI Overviews appeared for all seven tracked buyer prompts, and none cited nr10.com. The report recorded the prompts, whether an Overview appeared, whether we were cited, and the domains cited instead. Those fields made the gap inspectable rather than leaving us with a general sense that we needed “more visibility.”
That is a historical Google AI Overview baseline. It is not a ChatGPT benchmark, a current visibility claim, or proof that we have increased ChatGPT recommendations. We do not have a verified ChatGPT uplift to claim here.
The practical lesson is the measurement design. For a ChatGPT program, we would create a separate record of the questions tested, the answers returned, the brands recommended, the visible sources when provided, and any factual errors about the brand. We would then connect proposed page changes to the specific gaps observed.
This is also why we reject publishing volume as a strategy. The internal system gives each page a buyer, a purpose, and a review path. Producing more pages without those decisions would not tell us whether we were becoming a better answer.
Keep technical work in scope without selling a schema shortcut
Our SEO/AEO/GEO service includes technical and content audits, on-page optimization, schema, and internal-link work. In a ChatGPT-focused brief, we would use that foundation to check that the intended public pages are accessible, coherent, and connected to the rest of the site. We would also review access and indexing controls before recommending changes. We would not open private content to crawlers as a visibility tactic.
Structured data should describe what is actually on the page. Product information belongs with the product, service information with the service, and article information with the guide. FAQs should answer real questions rather than provide another place to repeat a target phrase.
We do not sell schema as a ChatGPT placement mechanism. It is part of maintaining understandable, consistent public information, not evidence that an assistant will recommend the brand. The same restraint applies to third-party proof: use genuine reviews, coverage, and references where they exist, but do not manufacture endorsements to fill a perceived authority gap.
Measure mentions, citations, and recommendations separately
For a proposed ChatGPT baseline, we would keep the prompt wording, date, available model or mode information, and whether web search was used alongside the saved answer. We would use fresh conversations and document the test conditions so a later review has context.
The review should distinguish several observations:
| Observation | What we would record |
|---|---|
| Brand mention | The answer names the brand, without necessarily endorsing it. |
| Recommendation | The answer presents the brand as a fit for the buyer’s stated need. |
| Citation | A visible source link points to a brand page or another source supporting the answer. |
| Accuracy | The answer describes the offer, audience, and limitations correctly. |
| Business response | Identifiable referral visits and qualified inquiries, where tracking can connect them. |
We would report these against the defined test set, not describe a small sample as the brand’s share of all ChatGPT answers. A citation without a recommendation and a recommendation without a visible citation are different observations. Neither should be silently counted as the other.
Changes also need interpretation. We would retest, preserve the answers, and look for repeated patterns rather than declare success from a single favorable screenshot. An observed improvement after an edit is worth investigating, but it does not by itself prove that the edit caused it.
A practical first scope for DTC and B2B brands
We would start with a buyer-question set and a documented baseline, then map each gap to existing pages. If the right page already exists, the recommendation may be a clearer answer, corrected product facts, stronger evidence, or better links. A new article earns its place only when it serves a distinct question the site does not already answer.
For established pages, we check search performance before changing them. Useful additions should not come at the cost of an unnecessary URL change or a rewrite that discards existing value. After the approved changes, we would repeat the ChatGPT tests separately from the Google search and AI Overview checks.
The deliverable is a prioritized set of decisions with evidence behind it: what the brand is currently being recommended for, where it is absent or misrepresented, which pages need attention, and what to measure next. That connects to our SEO, AEO and GEO work without pretending that every search surface behaves the same way.
Frequently asked questions
What does a ChatGPT visibility agency do for brands?
A ChatGPT visibility agency should connect buyer questions to brand facts, public pages, proof, and a platform-specific testing plan. At #10, we would separate unprompted recommendations from branded queries and report the evidence behind each proposed change. We do not promise guaranteed inclusion in answers.
Is answer engine optimization the same as ChatGPT SEO?
Answer engine optimization focuses on making answers clear and extractable. ChatGPT SEO also asks whether the brand is represented accurately and recommended for relevant buying situations. We treat the two as connected work, with separate observations for mentions, citations, recommendations, and business response.
How does AEO for ecommerce apply to product pages?
We use AEO for ecommerce to make product differences, selection advice, suitability, and policies easier to understand. The answer should lead to accurate product information and a clear buying path. We would check existing product and collection pages before recommending new educational content.
Does learning how to get cited in AI Overviews also explain how to appear in ChatGPT?
No. The content may share useful foundations, but Google AI Overviews and ChatGPT need separate tests and records. Our own historical AI Overview citation baseline is not evidence of ChatGPT performance. We would not combine the two into an unexplained visibility score.
Can a generative engine optimization agency guarantee ChatGPT recommendations?
We would not offer that guarantee. Our scope is the work we can substantiate: auditing the brand’s information, improving relevant pages, grounding claims in evidence, and documenting the answers returned during testing. Placement promises go beyond that evidence.
Give the right buyer a reason to choose you
ChatGPT SEO should make the brand’s fit more explicit, not make its claims more aggressive. We start with a buying situation the business can genuinely serve, connect it to useful public answers and verifiable proof, and test whether the resulting recommendations are accurate.
If your team needs that baseline and a practical plan for what to change, talk to #10. We will begin with the questions your buyers ask and the evidence your business can stand behind.
